Multiple Fuzzy Classification Systems
Fuzzy classi ers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scienti c and business applications. Fuzzy classi ers use fuzzy rules and do not require assumptions common to statistical classi cation. Rough set theory is useful when da...
Guardado en:
| Autor principal: | |
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| Formato: | Livre numérique |
| Lenguaje: | Anglais |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Colección: | Studies in Fuzziness and Soft Computing
288 |
| Acceso en línea: | Accès sur la plateforme de l'éditeur Accès sur la plateforme de l'éditeur (Springer) Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Multiple Fuzzy Classification Systems, Texte imprimé, 9783642306037 • Multiple Fuzzy Classification Systems, Texte imprimé, 9783642436574 • Multiple Fuzzy Classification Systems, Texte imprimé, 9783642306051 |
| Sumario: | Fuzzy classi ers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scienti c and business applications. Fuzzy classi ers use fuzzy rules and do not require assumptions common to statistical classi cation. Rough set theory is useful when data sets are incomplete. It de nes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classi cation. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a nite set of learning models, usually weak learners. The present book discusses the three aforementioned elds fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classi cation ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. |
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| Notas: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783642306044 |
| ISSN: | 1860-0808 |
| Acceso: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |